Preoperative nutritional risk index and postoperative one-year skeletal muscle loss can predict the prognosis of patients with gastric adenocarcinoma: a registry-based study

Association Between Changes in Skeletal Muscle Quality and Prognosis in Postoperative Patients with Early Gastric Cancer

BACKGROUND

Identifying accurate prognostic factors is crucial for postoperative management of early gastric cancer (EGC) patients. Skeletal muscle quality (SMQ), defined by muscle density on computed tomography (CT) images, has been proposed as a novel prognostic factor. This study compared the prognostic significance of SMQ changes with the well-established factor of body weight (BW) loss in the postoperative EGC setting.

METHODS

This single-center retrospective study included 297 postoperative EGC patients (median age 69 years, 68.4% male) who had preoperative and 1-year-postoperative gastrectomy CT images. SMQ was defined as the modified intramuscular adipose tissue content (mIMAC = skeletal muscle density-subcutaneous fat density on CT images) and the change as ΔmIMAC. Log-rank test, Kaplan-Meier survival, and Cox proportional hazards regression analyses were used to assess the associations between prognosis and either ΔmIMAC or BW change (ΔBW). Prognosis prediction by ΔmIMAC and ΔBW was compared by using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

ΔmIMAC was significantly associated with prognosis (log-rank test; P = 0.037), but ΔBW was not (P = 0.243). Prognosis was significantly poorer in the severely decreased mIMAC group than in the preserved group (multivariate Cox proportional hazards regression analysis; P = 0.030) but was unaffected by BW changes (P = 0.697). The AUC indicated a higher prognostic value for ΔmIMAC than ΔBW (ΔmIMAC: AUC = 0.697, ΔBW: AUC = 0.542).

CONCLUSIONS

One-year post-gastrectomy SMQ changes may be better prognostic EGC predictors than BW changes.

Postoperative sarcopenia increases both gastric cancer and other-cause mortality in older adults undergoing radical gastrectomy for cancer

BACKGROUND & AIMS

Preoperative sarcopenia in gastric cancer is associated with increased postoperative complications and reduced long-term survival. However, the association between postoperative sarcopenia and long-term outcomes remains unclear. Therefore, this study aims to clarify the association between sarcopenia after gastrectomy for gastric cancer and survival outcomes.

METHODS

This retrospective study included 1512 patients aged ≥65 who underwent curative gastric resection for clinical stage I-III primary gastric cancer during 2008-2018. Sarcopenia was assessed preoperatively by measuring arm muscle area and grip strength, which was repeated 1 month after surgery. We compared the clinical characteristics, surgical treatments, and long-term outcomes between the postoperative normal and sarcopenia groups.

RESULTS

Sarcopenia was observed in 173 and 305 patients pre- and postoperatively, respectively. Factors increasing the risk of postoperative sarcopenia included age of ≥75, lower preoperative body mass index, diabetes, and clinical stage II/III gastric cancer. Patients with postoperative sarcopenia after surgery had a significantly lower overall survival rate (hazard ratio [HR] 2.596, p < 0.001). Furthermore, postoperative sarcopenia was linked to decreased overall survival in patients with (HR 2.813, p = 0.002) and without (HR 1.925, p < 0.001) preoperative sarcopenia. Cumulative incidence showed significantly higher rates of deaths due to gastric cancer (HR 1.928, p < 0.001) and other causes (HR 2.736, p < 0.001) in the postoperative sarcopenia group.

CONCLUSIONS

Postoperative sarcopenia in gastric cancer is linked to an increased risk of death due to cancer and other causes, underscoring the importance of perioperative sarcopenia management strategies.

Effects of postoperative oral elemental nutritional supplement on skeletal muscle loss after gastrectomy for gastric cancer

BACKGROUND

We previously showed that daily nutritional intervention with an oral elemental diet (ED) at 300 kcal/day for 6-8 weeks postoperatively decreased the percentage of body weight loss (%BWL), and that the effect was maintained for 1 year. This post hoc analysis aimed to determine whether this intervention decreased skeletal muscle mass loss 1-year post-gastrectomy.

METHODS

Data from consecutive, untreated patients with histopathologically confirmed stage I-III gastric adenocarcinoma who planned to undergo total gastrectomy (TG) or distal gastrectomy (DG) and were enrolled in a previously published randomized trial were used. The primary endpoint was the percentage of skeletal muscle mass index (%SMI) loss from baseline at 1 year postoperatively, based on abdominal computed tomography images obtained preoperatively and at 1 year postoperatively.

RESULTS

The overall median %SMI loss was lower in the ED versus control group, but the difference was not significant. The difference in %SMI loss in the ED and control groups was greater in patients with TG (10.1 vs. 13.0; P = 0.12) than in those with DG (5.5 vs. 6.8; P = 0.69). A correlation was observed between %BWL and %SMI loss in both groups (ED group, coefficient 0.591; control group, coefficient 0.644; P < 0.001 for both). Type of gastrectomy (coefficient 7.38; P = 0.001) and disease stage (coefficient - 6.43; P = 0.04) were independent predictors of postoperative skeletal muscle mass loss.

CONCLUSION

ED administration for 6-8 weeks following gastrectomy had no inhibitory effect on skeletal muscle loss at 1 year postoperatively.

CLINICAL TRIAL REGISTRATION

UMIN000023455.

BMI trajectories, associations with outcomes and predictors in elderly gastric cancer patients undergoing radical gastrectomy: a prospective longitudinal observation study

OBJECTIVES

Elderly gastric cancer patients undergoing radical gastrectomy are prone to experience unexpected weight loss. Preoperative weight risk prediction may be a promising way to prevent weight loss and improve prognosis. The objectives of this study were to explore the BMI trajectory of elderly gastric cancer patients one year after surgery, evaluate theirs the association with outcomes, and explore their related predictors, so as to provide evidence for weight management and prognosis improvement.

METHODS

412 gastric cancer patients were included and recorded BMI at 6 time points. The trajectories of BMI were analyzed by growth mixture modeling, and the associations of BMI trajectories with outcomes as well as their predictors were investigated by regression models.

RESULTS

We identified 3 classes of BMI trajectories: the "slow-decreasing BMI", "rapid-decreasing BMI" and "maintaining BMI". Compared with class1, patients in class 2 were more likely to have a higher frequency of readmission within 1-year(β = 0.59, 95%CI: 0.29, 0.89, P < 0.001) and a higher rate of mortality within 1-year(β = 24.74, 95%CI: 9.60, 63.74, P < 0.001) ; patients in class 3 were more likely to have a higher quality of life (β=-10.46, 95%CI: -17.70, -3.22, P = 0.005) and fewer readmission times within one year (β=-0.43, 95%CI: -0.77, -0.09, P = 0.015). Predictors of decreasing BMI trajectories were TNM stage, comorbidity, anxiety, family cohesion and social support(P < 0.05).

CONCLUSIONS

Our findings can provide a basis for screening high-risk elderly gastric cancer patients with poor prognosis, implementing risk stratification, formulating accurate weight management programs and improving prognosis.

IMPLICATIONS FOR CANCER SURVIVORS

The results of our study can provide gastric cancer survivors with preoperative risk screening based on predictive factors so that nutritional support and weight management can be implemented in a timely manner to improve prognosis.

Nutritional risk index predicts the prognosis of gastric cancer patients with pyloric stenosis who received preoperative parenteral nutrition

Patients with gastric cancer with pyloric stenosis frequently have poor nutritional status and preoperative parenteral nutrition has been a common treatment strategy. The present study aimed to explore the predictive ability of the nutritional risk index (NRI) regarding the prognosis of patients with gastric cancer and pyloric stenosis who received preoperative parenteral nutrition. A total of 194 patients with gastric cancer with pyloric stenosis who received preoperative parenteral nutrition at Tthe Second People's Hospital of Neijiang (Neijiang, China) between January 2016 and December 2021 were included. At the same time, 221 patients with gastric cancer without pyloric stenosis who received surgery during the same period were also collected and the clinicopathological characteristics of the patients were compared. The optimal cut-off value of the NRI was determined from the receiver operating characteristic curve and prognostic factors were identified by survival analysis. Finally, a nomogram was constructed to predict the survival probability of patients with gastric cancer. The results indicated that patients with pyloric stenosis exhibited a wide range of unfavorable pathological characteristics and blood parameters. In addition, their overall survival (OS) was significantly worse (P<0.001). Among the patients with pyloric stenosis, there were 120 patients (61.9%) with an NRI <93.42 and 74 patients (38.1%) with NRI ≥93.42. Furthermore, patients with an NRI <93.42 had poorer OS (34.37 months vs. not reached, P=0.004). Of note, age, tumor size, radical resection, NRI and TNM stage were determined to be independent prognostic factors for OS. The C-index of the nomogram was 0.760 (95%CI: 0.688-0.832). In conclusion, the NRI was indicated to be an accurate score reflecting the nutritional status of patients, which was able to predict the clinical outcomes of patients with gastric cancer with pyloric stricture who received preoperative parenteral nutrition. Patients with a low NRI had shorter survival times.

Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

Prognostic artificial intelligence model to predict 5 year survival at 1 year after gastric cancer surgery based on nutrition and body morphometry

BACKGROUND

Personalized survival prediction is important in gastric cancer patients after gastrectomy based on large datasets with many variables including time-varying factors in nutrition and body morphometry. One year after gastrectomy might be the optimal timing to predict long-term survival because most patients experience significant nutritional change, muscle loss, and postoperative changes in the first year after gastrectomy. We aimed to develop a personalized prognostic artificial intelligence (AI) model to predict 5 year survival at 1 year after gastrectomy.

METHODS

From a prospectively built gastric surgery registry from a tertiary hospital, 4025 gastric cancer patients (mean age 56.1 ± 10.9, 36.2% females) treated gastrectomy and survived more than a year were selected. Eighty-nine variables including clinical and derived time-varying variables were used as input variables. We proposed a multi-tree extreme gradient boosting (XGBoost) algorithm, an ensemble AI algorithm based on 100 datasets derived from repeated five-fold cross-validation. Internal validation was performed in split datasets (n = 1121) by comparing our proposed model and six other AI algorithms. External validation was performed in 590 patients from other hospitals (mean age 55.9 ± 11.2, 37.3% females). We performed a sensitivity analysis to analyse the effect of the nutritional and fat/muscle indices using a leave-one-out method.

RESULTS

In the internal validation, our proposed model showed AUROC of 0.8237, which outperformed the other AI algorithms (0.7988-0.8165), 80.00% sensitivity, 72.34% specificity, and 76.17% balanced accuracy. In the external validation, our model showed AUROC of 0.8903, 86.96% sensitivity, 74.60% specificity, and 80.78% balanced accuracy. Sensitivity analysis demonstrated that the nutritional and fat/muscle indices influenced the balanced accuracy by 0.31% and 6.29% in the internal and external validation set, respectively. Our developed AI model was published on a website for personalized survival prediction.

CONCLUSIONS

Our proposed AI model provides substantially good performance in predicting 5 year survival at 1 year after gastric cancer surgery. The nutritional and fat/muscle indices contributed to increase the prediction performance of our AI model.

Prognostic role of pretreatment skeletal muscle index in gastric cancer patients: A meta-analysis

Background: The association between pretreatment skeletal muscle index (SMI) and long-term survival of gastric cancer patients remains unclear up to now. The aim of this meta-analysis was to identify the prognostic value of pretreatment SMI in gastric cancer. Methods: The PubMed, EMBASE and Web of Science electronic databases were searched up to 5 June 2022 for relevant studies. The primary outcome was overall survival (OS) and the second outcomes were disease-free survival (DFS) and cancer-specific survival (CSS). The hazard ratios (HRs) and 95% confidence intervals (CIs) were combined to assess the relationship between pretreatment SMI and survival of gastric cancer patients. All statistical analyses were conducted by STATA 15.0 software. Results: A total of 31 retrospective studies involving 12,434 patients were enrolled in this meta-analysis. The pooled results demonstrated that lower pretreatment was significantly associated with poorer OS (HR = 1.53, p < 0.001). Besides, lower pretreatment SMI was also related with worse DFS (HR = 1.39, p < 0.001) and CSS (HR = 1.96, p < 0.001). Conclusion: Pretreatment SMI was significantly associated with prognosis of gastric cancer patients and lower SMI predicted worse survival. However, more prospective high-quality studies are still needed to verify our findings.

A novel nomogram based on the prognostic nutritional index for predicting postoperative outcomes in patients with stage I-III gastric cancer undergoing robotic radical gastrectomy

BACKGROUND

The inflammation and nutrition status are crucial factors influencing the outcome of patients with gastric cancer. This study aims to investigate the prognostic value of the preoperative prognostic nutritional index (PNI) in patients with stage I-III gastric cancer undergoing robotic radical gastrectomy combined with Enhanced Recovery after Surgery (ERAS), and further to create a clinical prognosis prediction model.

STUDY

525 patients with stage I-III gastric cancer who underwent ERAS combined with RRG from July 2010 to June 2018 were included in this work, and were divided randomly into training and validating groups in a 7-to-3 ratio. The association between PNI and overall survival (OS) was assessed by Kaplan-Meier analysis and the log-rank test. Independent risk factors impacting postoperative survival were analyzed with the Cox proportional hazards regression model. A nomogram for predicting OS was constructed based on multivariate analysis, and its predictive performance was evaluated using Harrell's concordance index (C-index), calibration plots, ROC curve, decision curve analysis (DCA), and time-dependent ROC curve analysis.

RESULTS

Survival analyses revealed the presence of a significant correlation between low preoperative PNI and shortened postoperative survival (P = 0.001). According to multivariate analysis, postoperative complications (P < 0.001), pTNM stage (II: P = 0.007; III: P < 0.001), PNI (P = 0.048) and lymph node ratio (LNR) (P = 0.003) were independent prognostic factors in patients undergoing ERAS combined with RRG. The nomogram constructed based on PNI, pTNM stage, complications, and LNR was superior to the pTNM stage model in terms of predictive performance. The C-indexes of the nomogram model were respectively 0.765 and 0.754 in the training and testing set, while AUC values for 1-year, 3-year, and 5-year OS were 0.68, 0.71, and 0.74 in the training set and 0.60, 0.67, and 0.72 in the validation set.

CONCLUSION

Preoperative PNI is an independent prognostic factor for patients with stage I-III gastric cancer undergoing ERAS combined with robotic radical gastrectomy. Based on PNI, we constructed a nomogram for predicting postoperative outcomes of gastric cancer patients, which might be utilized clinically.

Optimizing the Choice for Adjuvant Chemotherapy in Gastric Cancer

Simple Summary

Gastric cancer is the fourth largest cause of tumor-related death worldwide. Despite advances in the management of resectable cancer and improvements in early diagnosis, especially in east Asia where screening campaigns are actively performed, many patients experience recurrence and die because of the disease. Adjuvant systemic chemotherapy is administered after radical surgery in order to reduce the risk of recurrence and death. The modality of administration and regimens of chemotherapy in this setting are different between Eastern and Western countries. In Asia, adjuvant chemotherapy is traditionally given after surgery, while in Europe it is commonly scheduled after preoperative chemotherapy and surgery (perioperative chemotherapy), and in Northern America it is usually combined with radiotherapy (chemoradiotherapy). All these approaches are sustained by well-designed phase III clinical studies, and none may be considered superior to the others in the absence of head-to-head comparisons. The identification of predictive and/or prognostic factors could help to select patients at higher risk of recurrence and those more likely to receive a benefit from the adjuvant treatment. This would allow clinicians to avoid the administration of undue toxicity to non-responder patients and even to reduce the cost of unnecessary treatment.

Abstract

Advances in the management of gastric cancer have improved patient survival in the last decade. Nonetheless, the number of patients relapsing and dying after a diagnosis of localized gastric cancer is still too high, even in early stages (10% in stage I). Adjuvant systemic chemotherapy has been proven to significantly improve outcomes. In the present article we have critically reviewed the clinical trials that guide the current clinical practice in the adjuvant treatment of patients affected by resectable gastric cancer, focusing on the different approaches worldwide, i.e., adjuvant chemotherapy, adjuvant chemoradiotherapy, and perioperative chemotherapy. We also delineate the clinical–pathological characteristics that are commonly taken into account to identify patients at a higher risk of recurrence and requiring adjuvant chemotherapy, and also describe novel biomarkers and therapeutic agents that might allow personalization of the treatment.

Preoperative Prognostic Nutritional Index May Be a Strong Predictor of Hepatocellular Carcinoma Recurrence Following Liver Transplantation

Purpose

Malnutrition is a major risk factor of immune dysfunction and poor outcome in cancer patients. The prognostic nutritional index (PNI), which is established by serum albumin level and peripheral lymphocyte count, was shown to correlate with prognosis of hepatocellular carcinoma (HCC) patients following liver resection and non-surgical interventions. The aim of this study was to analyze the predictive value of preoperative PNI in liver transplantation (LT) patients with HCC.

Patients and Methods

A total of 123 HCC patients that underwent LT were included in the analysis. The prognostic impact of preoperatively assessed clinical factors including the PNI on post-LT outcome was analyzed by uni- and multivariate analysis.

Results

Post-transplant tumor recurrence rates were 5.1% in high-PNI (> 42) and 55.6% in low-PNI (≤ 42) patients (p < 0.001). Preoperative high-PNI could be identified as a significant and independent promoter of both recurrence-free survival (hazard ratio [HR] = 10.12, 95% CI: 3.40–30.10; p < 0.001) and overall survival (HR = 1.69, 95% CI: 1.02–2.79; p = 0.004) following LT. Apart from that low-PNI proved to be a significant and independent predictor of microvascular tumor invasion (OR = 7.71, 95% CI: 3.17–18.76; p < 0.001). In contrast, no tumor morphology features including the Milan criteria revealed an independent prognostic value.

Conclusion

Our data indicate that preoperative PNI correlates with biological tumor aggressiveness and outcome following LT in HCC patients and may therefore be useful for refining oncologic risk stratification.

Nutritional Risk Index as a Prognostic Factor Predicts the Clinical Outcomes in Patients With Stage III Gastric Cancer

Objective

This study is aimed to determine the potential prognostic significance of nutritional risk index (NRI) in patients with stage III gastric cancer.

Methods

A total of 202 patients with stage III gastric cancer were enrolled in this study. NRI was an index based on ideal body weight, present body weight, and serum albumin levels. All patients were divided into two groups by receiver operating characteristic curve: low NRI group (NRI&lt;99) and high NRI group (NRI≥99). The relationship between NRI and clinicopathologic characteristics was evaluated by Chi-square test. The clinical survival outcome was analyzed by Kaplan-Meier method and compared using log-rank test. The univariate and multivariate analyses were used to detect the potential prognostic factors. A nomogram for individualized assessment of disease-free survival (DFS) and overall survival (OS). The calibration curve was used to evaluate the performance of the nomogram for predicted and the actual probability of survival time. The decision curve analysis was performed to assess the clinical utility of the nomogram by quantifying the net benefits at different threshold probabilities.

Results

The results indicated that NRI had prognostic significance by optimal cutoff value of 99. With regard to clinicopathologic characteristics, NRI showed significant relationship with age, weight, body mass index, total protein, albumin, albumin/globulin, prealbumin, glucose, white blood cell, neutrophils, lymphocyte, hemoglobin, red blood cell, hematocrit, total lymph nodes, and human epidermal growth factor receptor 2 (P&lt;0.05). Through the univariate and multivariate analyses, NRI, total lymph nodes, and tumor size were identified as the independent factor to predict the DFS and OS. The nomogram was used to predict the 1-, 3-, and 5-year survival probabilities, and the calibration curve showed that the prediction line matched the reference line well for 1-, 3-, and 5-year DFS and OS. Furthermore, the decision curve analysis also showed that the nomogram model yielded the best net benefit across the range of threshold probability for 1-, 3-, 5-year DFS and OS.

Conclusions

NRI is described as the potential prognostic factor for patients with stage III gastric cancer and is used to predict the survival and prognosis.

Artificial Intelligence Mortality Prediction Model for Gastric Cancer Surgery Based on Body Morphometry, Nutritional, and Surgical Information: Feasibility Study

The objective of this study is to develop a mortality prediction model for patients undergoing gastric cancer surgery based on body morphometry, nutritional, and surgical information. Using a prospectively built gastric surgery registry from the Asan Medical Center (AMC), 621 gastric cancer patients, who were treated with surgery with no recurrence of cancer, were selected for the development of the prediction model. Input features (i.e., body morphometry, nutritional, surgical, and clinicopathologic information) were selected in the collected data based on the XGBoost analysis results and experts’ opinions. A convolutional neural network (CNN) framework was developed to predict the mortality of patients undergoing gastric cancer surgery. Internal validation was performed in split datasets of the AMC, whereas external validation was performed in patients in the Ajou University Hospital. Fifteen features were selected for the prediction of survival probability based on the XGBoost analysis results and experts’ suggestions. Accuracy, F1 score, and area under the curve of our CNN model were 0.900, 0.909, and 0.900 in the internal validation set and 0.879, 0.882, and 0.881 in the external validation set, respectively. Our developed CNN model was published on a website where anyone could predict mortality using individual patients’ data. Our CNN model provides substantially good performance in predicting mortality in patients undergoing surgery for gastric cancer, mainly based on body morphometry, nutritional, and surgical information. Using the web application, clinicians and gastric cancer patients will be able to efficiently manage mortality risk factors.